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Analysis of software risk across the six risk criteria: a schedule risk factor, b product risk factor, c platform risk factor, d personnel risk factor, e process risk factor and f reuse risk factor
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Risk management is a vital factor for ensuring better quality software development processes. Moreover, risks are the events that could adversely affect the organization activities or the development of projects. Effective prioritization of software project risks play a significant role in determining whether the project will be successful in terms...
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... This approach was then applied to RA for SRPs, showcasing a model grounded in the proposed methodology. Suresh and Dillibabu 27 proposed an innovative hybrid fuzzy-based machine learning mechanism tailored for RA in software projects. This hybrid scheme facilitated the identification and ranking of major software project risks, thereby supporting decision-making throughout the software project lifecycle. ...
This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.
... In a competitive economy environment, the problem of how to determine an appropriate investment allocation of limited resources to select low-risk and high-benefit set of projects is called project portfolio selection (PPS) problem, which is crucial for a/an company/organization (Boyd et al., 2017;ForouzeshNejad, 2023;Kolm et al., 2014). However, risks triggered by multiple uncertainties, such as resource shortages (Afzal et al., 2023;Bai et al., 2020), organizational instability (Mahmoudi et al., 2022), etc., can seriously affect the realization of project benefits, so the problem of PPS considering risks has been widely concerned (Asadabadi & Zwikael, 2021;Hofman et al., 2018;Suresh & Dillibabu, 2020;X. Wang et al., 2021). ...
The problem of project portfolio selection considering risk propagation is a new research topic, but it overlooks the fact that the risk propagation process can be hindered by resource mobilisation within candidate projects, which is defined as “risk absorption” in this paper. Neglecting this process may lead to the risk propagation process being overestimated. This study aims to investigate the problem of project portfolio selection considering risk absorption, and to work on selecting stable project portfolio. To address this issue, the article first examines the process of risk absorption among projects and calculates the amount of risk absorbed based on the strength of the interactions between the projects selected. Secondly, the total risk intensity of the project is given by calculating the risk propagation intensity and self-incurred risk intensity. Finally, a dual-objective project portfolio selection model considering risk absorption is developed by combining the risk intensity and net present value of the project. The analysis of a case study demonstrates that 1) considering risk absorption can reduce the probability of project risks occurring due to being propagated, 2) considering risk absorption will prioritize projects with higher returns, albeit accompanied by relatively higher risks.
... The lack of more significant and impactful cost drivers leads to less reliable estimates, highlighting a critical research gap affecting the ability to enhance estimation accuracy within GSD [22]. While key to successful software project completion, risk assessment remains underemphasized in the planning phase despite its potential to fail projects [33][34][35][36][37]. ...
Accurate software cost estimation in Global Software Development (GSD) remains challenging due to reliance on historical data and expert judgments. Traditional models, such as the Constructive Cost Model (COCOMO II), rely heavily on historical and accurate data. In addition, expert judgment is required to set many input parameters, which can introduce subjectivity and variability in the estimation process. Consequently, there is a need to improve the current GSD models to mitigate reliance on historical data, subjectivity in expert judgment, inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns. This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks (ANN) to address these challenges. The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts. This article compares the effectiveness of the proposed model with state-of-the-art machine learning-based models for software cost estimation. Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy, outperforming existing state-of-the-art models. The findings indicate the potential of combining COCOMO II, ANN, and additional GSD-based cost drivers to transform cost estimation in GSD.
... The study in [27] introduced a novel approach to software defect prediction. The study in [28] developed a new hybrid machine-learning mechanism for risk assessment in software projects. ...
... In the Check steps, only a few papers have mentioned how AI is implemented in their study cases. Reference [25] evaluates data models to choose the best security approach, while studies in [27] and [28] evaluate data results to compare their new methods with other existing methods. However, papers have yet to be found that implement AI in the Act steps. ...
... As asserted in [48], the use of AI in the hotel industry is not limited to enhancing hotel services but also to adapting to other AI models, facilitating communication for both internal and external components, managing information, and handling multiple tasks to increase hotel performance. Assessment is commonly associated with the work of AI, affirmed in [28], by using the adaptive neuro-fuzzy inference system-based multi-criteria decisionmaking (ANFIS MCDM) and intuitionistic fuzzy-based TODIM (IF-TODIM) approach to assess risk in software projects. The [49] expressed that it is possible to detect a fault in the system using the deep neural network and genetic algorithm method and, as it will enhance its efficiency of defect prediction. ...
Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature review
... Although some researchers argue that this approach is inadequate, since the emergence of agile development, there have been calls for exploring formal assessment of risks to combat the lack of risk assessment in agile projects (Anes et al., 2020). Extant literature offers a wide range of models to assess risks (Persson et al., 2009;Suresh & Dillibabu, 2020) and specifically for agile teams (Anes et al., 2020;Lopes et al., 2021;Odzaly et al., 2018). The existing assessment models cover different types of risks, e.g., implementation risks (Lyytinen, 1987), requirements-related risks (Ramesh et al., 2010), distributed development risks (Persson et al., 2009), risks to effective knowledge sharing (Ghobadi and Mathiassen, 2016), and information security risks (Kuzminykh et al., 2021). ...
... First, our action case study shows that causal mapping is useful for revealing role-specific explanations of software project risks in agile teams. Numerous risk assessment tools and models of software project risks have been proposed recently, but many still fail to consider the mutual implications of risks (Lopes et al., 2021;Suresh & Dillibabu, 2020;Tavares et al., 2021). This study extends previous research that found causal mapping can unfold the mutual implications of risks (Ackermann et al., 2014;Ackermann & Eden, 2020;Williams, 2017). ...
Agile teams must deal with many risks to their software project's resources and schedule. This challenge is exacerbated in large-scale agile development by the increasingly specialized team roles with inherently different explanations of the software project's risks. Against this backdrop, we report an action case study of how an agile team can understand and manage different explanations of their project risks. We used the causal mapping technique to understand how a team's six different roles explain their software project risks and assessed these maps' usefulness with the team. From this action case study, we present two findings. First, causal mapping is useful for revealing role-specific explanations of software project risks in agile teams. Second, agile teams can use role-specific causal maps to juxtapose their explanations of software project risks. We discuss how these findings imply a caveat for agile teams seeking to understand and manage team-generalized software project risks and ignoring idiosyncratic explanations of software project risks.
... Numerous techniques have been proposed to determine the weight of software risk [32,35,37]. This paper presents a straightforward risk evaluation model that utilizes the proposed entropy. ...
The measurement of uncertainty has been an important topic of research. In Dempster’s framework, Deng entropy serves as a reliable tool for such measurements. However, it fails to consider more comprehensive information, resulting in the loss of critical data. An improved belief entropy is proposed in this paper, which preserves all the merits of Deng entropy. When there is only a single element, it can be degraded to Shannon entropy. When dealing with multiple elements, the partitioning method employed for mass functions makes it more responsive and efficient than alternative measures of uncertainty. Some numerical examples are given to further illustrate the effectiveness and applicability of the proposed entropy measure. Additionally, a case study is conducted on software risk analysis, demonstrating the practical value and relevance of the proposed method in real-world scenarios.
... Suresh ve Dillibabu (2020), yazılım geliştirme projelerinin risklerini daha iyi değerlendirmek için bulanık DEMATEL, uyarlanabilir nöro-bulanık çıkarım sistemi tabanlı çok kriterli karar verme (ANFIS MCDM) ile sezgisel bulanık tabanlı TODIM (IF-TODIM) yaklaşımlarını kullanarak karma bir model geliştirmiştir. Literatürdeki diğer yöntemlerle karşılaştırıldığında, kendi yöntemlerinin çizelgeleme, ürün, personel, platform, süreç ve yeniden kullanılabilirlik kriterleri ile daha iyi risk yönetimi sunduğunu ifade etmişlerdir [4]. Yazılım projelerinde risk yönetimi ile ilgili bir başka çalışmada, yazılım mühendisliğinde proje geliştirme süreçleri risk sürdürülebilirliğinin yönetimi açısından incelenmiştir. ...
Yazılım geliştirme projelerinin yönetilmesi, karmaşık iş adımlarını içermesi ve kullanılan kaynakların gerçek kişilerden oluşması nedeniyle zordur. Projenin kendi özellikleri ile birlikte organizasyonel değişkenler ve geliştirici ekibin yetkinlikleri, projenin başarısını doğrudan etkileyecek faktörler arasındadır. Yazılım geliştirme süreç yöntem seçimi probleminin çözümünde bu üç faktörün kullanılması ile yazılım geliştirme projelerinin başarısının artacağı düşünülmektedir. Yazılım projelerinin yönetiminde "Çevik ve Şelale (Geleneksel) Yöntemler" dâhil olmak üzere farklı süreç yöntemleri kullanılmaktadır. Geliştirme ekiplerinin yeteneklerine göre projelere atanması ve projelerin kendi aralarında sıralanması önemli bir noktadır. Bu çalışmada ilk adım olarak, bulanık AHP metodu kullanılarak kriterlerin ağırlıkları belirlenmiştir. Daha sonra projelerin ve çalışanların sıralanmasında bulanık WASPAS, bulanık EDAS ve Aralık Değerli Nötrosofik Z Sayıları (NZN) yöntemleri kullanılmıştır. Bu araştırmanın sonuçlarına göre, yazılım geliştirme yöntemi kararının uygulanması halinde hata sayısı ve çözümlerinin kişi-gün değerleri gerçek değerler ile kıyaslandığında %4-%6, %6-%8 ve %6-%8,7 oranında iyileştirilebileceği tespit edilmiştir.
... [16] Starting with the analysis of the learning sets, known as algorithms for learning, creates bad functions for predicting the values of efficiency. [11] The system cannot give instructions to any new implants after adequate training. The training algorithm could also be compared to the output to a planned correct result and, to find fault, modified models accordingly. ...
The main objective of this paper is to be lowered to a minimum of delay and not to provide an early forecast of cancer of lung cancer in patients, but also and not to reduce the need for biopsy. In addition, it gives and comparisons between different algorithms for classification. Existing models use a CT scan to predict lung cancer but are also well equipped with sufficient data to predict precisely. The model was developed based on a support vector machine, which provides 92 % accuracy. Because it is required solutions, for to be increased right speed for prognosis of cancer of lung cancer. The proposed solution uses a set of data for classification and has approximately 300 observations. The model is developed for different algorithms for classification, such as arbitrary attendant’s vectors, K-nearest-neighbors, and Naive-Bayes are compared to a - great accuracy. It provides an early prognosis of cancer of lung cancer with high accuracy. Therefore, the proposed decision model based on Naïve Bayes achieved the highest degree of accuracy of 95.23%.
... All ( ) k ij X from k experts are then added together, and averaged. The calculation formula to calculate the n n arithmetic mean matrix [89] is given below; ...
With COVID-19 still making headlines around the world, many people currently feel uncertain about many aspects of life, including family financial planning and wealth management. Financial planning is important at all times, but it becomes essential during a crisis such as the coronavirus pandemic, which has disrupted people’s finances. Some economic consequences are already apparent, but the financially-induced stress caused by the uncertainty is less visible. With the increase in family wealth and size of organizations, there is a comparable increase in their assets. There is considerable demand for professionals to manage these assets and coordinate investment activities in order to maintain growth. This raises the issue of how to increase a wealth management bank’s competitive advantages. This study approached the issue by using experts and the application of fuzzy logic and decision-making trial and evaluation laboratory and multiple criteria decision-making to segment a set of the selection criteria used by prospective customers, to select a wealth management bank that can effectively manage personal wealth. The results showed that the management’s learning and growth perspective was the most important factor in respondents’ selection of a wealth management bank. This paper also provides managerial practice implications.
... Requirement risk, user risk, developer risk, project management risk, development risk, environment risk [67] Personnel risk, system requirement risk, schedules and budgets risk, developing technology risk, external resource risk, performance risk [68] Requirements risk, estimations risk, planning risk, team organization risk, project management risk [69] Schedule risk, product risk, platform risk, personnel risk, process risk, reuse risk [70] Organizational environment risk, user risk, requirement risk, project complexity risk, team risk, planning risk [71] Requirement specification, design and implementation, integration and testing, development process and system management process, management methods, work environment, resources, contract and program interface [72] Corporate environment, sponsorship and ownership, relationship management, project management, scope, requirements, funding, scheduling and planning, development process, personnel and staffing, technology, external dependencies [73] Risk factors always change with the environment. Meanwhile, different software projects have different risks. ...
... In this section, we compare the constructed DDERM model with several existing methods, including Fuzzy set theory and hierarchical structure [68,77], Fuzzy DEMATEL, FM-CDM, TODIM approaches [69], DEMATEL, ANFIS MCDM and F-TODIM approaches [70], entropy-based method [67]. The comparison are shown in Table 12. ...
... Besides, the evaluations of different experts have no effect on the weights of other experts. (2) In [69,70], multiple modifications to the judgment matrix are frequently required because the judgment matrix created during the evaluation process is not completely consistent. The judgment matrix needs to be modified more than 4 times. ...
Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.
